Description Usage Arguments Value Author(s) Examples

Enables doing classification schemes such as ordinary 10-fold,
100 permutations 5-fold, and leave one out cross-validation.
Processing in parallel is possible by leveraging the package `BiocParallel`

.

1 2 3 4 5 6 7 8 9 10 11 | ```
## S4 method for signature 'matrix'
runTests(measurements, classes, ...)
## S4 method for signature 'DataFrame'
runTests(measurements, classes, datasetName, classificationName,
validation = c("permute", "leaveOut", "fold"),
permutePartition = c("fold", "split"),
permutations = 100, percent = 25, folds = 5, leave = 2,
seed, parallelParams = bpparam(),
params = list(SelectParams(), TrainParams(), PredictParams()), verbose = 1)
## S4 method for signature 'MultiAssayExperiment'
runTests(measurements, targets = names(measurements), ...)
``` |

`measurements` |
Either a |

`classes` |
Either a vector of class labels of class |

`targets` |
If |

`...` |
Variables not used by the |

`datasetName` |
A name associated with the data set used. |

`classificationName` |
A name associated with the classification. |

`validation` |
Default: |

`permutePartition` |
Default: |

`permutations` |
Default: 100. Relevant when permuting is used. The number of times to do reordering of the samples before splitting or folding them. |

`percent` |
Default: 25. Used when permutation with the split method is chosen. The percentage of samples to be in the test set. |

`folds` |
Default: 5. Relevant when repeated permutations are done and |

`leave` |
Default: 2. Relevant when leave-k-out cross-validation is used. The number of samples to leave for testing. |

`seed` |
The random number generator used for repeated resampling will use this seed, if it is provided. Allows reproducibility of repeated usage on the same input data. |

`parallelParams` |
An object of class |

`params` |
A |

`verbose` |
Default: 1. A number between 0 and 3 for the amount of progress messages to give. A higher number will produce more messages as more lower-level functions print messages. |

If the predictor function made a single prediction, then an object of class
`ClassifyResult`

. If the predictor function made a set of predictions, then
a list of such objects.

Dario Strbenac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
if(require(sparsediscrim))
{
data(asthma)
resubstituteParams <- ResubstituteParams(nFeatures = seq(5, 25, 5),
performanceType = "balanced error",
better = "lower")
runTests(measurements, classes, "Asthma", "Different Means",
permutations = 5,
params = list(SelectParams(limmaSelection, "Moderated t Statistic",
resubstituteParams = resubstituteParams),
TrainParams(DLDAtrainInterface),
PredictParams(DLDApredictInterface,
getClasses = function(result) result[["class"]])))
}
``` |

ClassifyR documentation built on July 8, 2018, 2 a.m.

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